1 citations · 2 across the 9 of their papers we have counts for
9 papers
Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning
Chenxiang Ma, Xinyi Chen, Yujie Wu +2
Spiking neural networks (SNNs), recognized as an energy-efficient alternative to traditional artificial neural networks (ANNs), have advanced rapidly through the scaling of models…
Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Chenxiang Ma +4
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct trai…
Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan +1
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training o…
Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing
Xinyi Chen, Chenxiang Ma, Yujie Wu +2
Temporal processing is vital for extracting meaningful information from time-varying signals. Recent advancements in Spiking Neural Networks (SNNs) have shown immense promise in ef…
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Chenxiang Ma, Xinyi Chen, Yanchen Li +7
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses…
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
Xiang Hao, Chenxiang Ma, Qu Yang +2
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved s…
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Xinyi Chen, Jibin Wu, Chenxiang Ma +4
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multiscale…
Scaling Supervised Local Learning with Augmented Auxiliary Networks
Chenxiang Ma, Jibin Wu, Chenyang Si +1
Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the updat…
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma +2
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signal…